The price and consumption of energy throughout the world has been increasing dramatically over recent years and is expected to continue along this trend in the years to come. According to a 2008 U.S. Department of Energy Annual Energy Outlook, residential energy consumption is expected to increase by approximately one percent per year for the next 20 years while energy prices slowly rise (see, Annual Energy Outlook, US Department of Energy, http://www.eia.doe.gov/oiaf/aeo/). Residential-related carbon dioxide emissions are also expected to increase. These trends clearly indicate the need for building technology solutions that lessen energy consumption.
To achieve lower energy consumption, existent appliances or devices can be replaced with more energy efficient alternatives, building occupants or owners can alter their behavior to reduce the use of energy-consuming devices, and automated building management solutions can control the operation of devices in the building so as to achieve less energy consumption or schedule operation for non-peak demand periods to reduce energy costs. All of these approaches are most effective when based on detailed knowledge of the amount of energy consumed by devices currently in the building and periods of operation of the devices. Such knowledge allows informed decisions as to how to reduce energy consumption. A major source of knowledge is available by monitoring energy consumption.
A number of systems exist for measuring energy consumption in a building and reporting this to users. See, for example, D. Parker, D. Hoak, A. Meier, R. Brown, “How much energy are we using? Potential of residential energy demand feedback devices”, Proc. Summer Study on Energy Efficiency in Buildings, 2006. Many known systems only report the total amount of electricity consumption for the entire building. To obtain truly detailed information that is most useful in determining how to achieve energy savings, the effect of individual devices on the total amount of energy consumption is needed. Most current systems, however, lack any disaggregated reporting of the overall consumption and use-patterns of individual devices and appliances. Some exceptions are systems that use separate measurement devices to measure the electricity consumption of, for example, a sub-circuit of the building, an individual wall outlet or even an individual device itself. Systems of this type, however, require a large number of meters and are both costly and cumbersome to install.
An alternative approach to obtaining information associated with energy consumption of discreet devices is the use of nonintrusive appliance load monitoring. Non-Intrusive Load Monitoring (NILM) derives its name from the fact that, the technique allows some level of individual load monitoring in a building without intruding (e.g., placing sensors or other devices) into the building. This general approach, also referred to as NIALM (Non-Intrusive Appliance Load Monitoring), has been studied extensively by researchers around the world, yielding promising results. Various approaches are disclosed in U.S. Pat. No. 4,858,141, issued to Hart et al., and C. Laughman, et al., “Power signature analysis”, IEEE Power and Energy Magazine, vol. 1, no. 2, pp. 56-63, 2003. More information on this type of approach is also available in U.S. Pat. No. 5,483,153 issued to Leeb, et al., U.S. Pat. No. 7,043,380 issued to Rodenberg, et al, U.S. Pat. No. 6,993,417 issued to Osann, Jr., U.S. Pat. No. 5,337,013 issued to Langer et al., U.S. Pat. No. 5,717,325 issued to Leeb, et al., and U.S. Pat. No. 6,993,417 issued to Osann, Jr. The usefulness of the approaches disclosed in the foregoing sources in real world building environments which have numerous devices operating in parallel has been minimally studied.
One publication, G. W. Hart, “Nonintrusive appliance load monitoring”, Proceedings of the IEEE, 80(12): 1870-1891, 1992, describes a method for utilizing normalized real and reactive power (“P” and “Q”, respectively) measurements from a main electrical feed of a residential building in NILM. In the disclosed approach, steady state power metrics (i.e., disregarding any transient, non-stable state) are used to describe in a distinct way the power draw of a number of home appliances. In other words, when an individual appliance changes state, for example from “off” to “on”, a unique change in the total P and Q of the house occurs. Hart referred to these changes as the appliance's “signature”, and described methods for correcting possible overlaps in the signature space by making use of appliance state transition models (e.g., an appliance cannot go from “off” to “on” and then again to “on”).
Leslie K. Norford and Steven B. Leeb, “Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms”, Energy and Buildings, 24(1):51-64, 1996, reported an improvement on Hart's “signature” technique which incorporated analysis of the startup transients of appliances coupled with the use of better algorithms for detecting when state transitions have occurred. See, also, Dong Luo, et al., “Monitoring HVAC equipment electrical loads from a centralized location—methods and field test results”, ASHRAE Transactions, 108(1):841-857, 2002.
C. Laughman, et al., “Power signature analysis”, Power and Energy Magazine, IEEE, 1(2):56-63, 2003, describes how the use of current harmonics can improve the process even further, allowing for the detection and classification of certain continuously variable loads. W. Wichakool, et al., “Resolving power consumption of variable power electronic loads using nonintrusive monitoring”, Power Electronics Specialists Conference, 2007, PESC 2007. IEEE, pages 2765-2771, 2007 presents further improvements to a solution for the problem of variable power electronics by using a spectral estimation method and a switching function technique. A summary and presentation of the achievements in this line of work can be found in S. R. Shaw, et al., “Nonintrusive load monitoring and diagnostics in power systems”, Instrumentation and Measurement, IEEE Transactions, 57(7): 1445-1454, 2008.
Other research has focused on utilizing NILM for monitoring the health of large appliances, by carefully analyzing any changes to startup transients and associated signatures as detailed by James Paris, “A framework for non-intrusive load monitoring and diagnostics”, Thesis, Massachusetts Institute of Technology, 2006, Thesis M. Eng.—Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006, R. Cox, et al., “Transient event detection for nonintrusive load monitoring and demand side management using voltage distortion”, Applied Power Electronics Conference and Exposition, 2006, APEC '06, Twenty-First Annual IEEE, page 7, 2006, and Leslie K. Norford and Steven B. Leeb, “Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms”, Energy and Buildings, 24(1):51-64, 1996.
Some efforts have been made to eliminate the need for a separate event detector. In one approach, a Kalman Filter is used to solve combinatorial Kalman Filter problems for each device in a building to determine the operational status of devices in a building. As the number of devices in the building increases, however, the complexity of such an approach increases hyperbolically. Moreover, such approaches incur some inaccuracy because only a small amount of output information is considered.
Efforts have also been made towards eliminating the need to collect current readings by inferring these from pure voltage measurements as reported by R. Cox, et al., “Transient event detection for nonintrusive load monitoring and demand side management using voltage distortion”, Applied Power Electronics Conference and Exposition, 2006, APEC '06, Twenty-First Annual IEEE, page 7 pp., 2006. Other efforts have focused on methods for identifying an operating appliance that do not require an appliance to change from one state to the other. Rather, appliance operation is detected during operation of the appliance. See, e.g., D. Srinivasan, et al., “Neural-network-based signature recognition for harmonic source identification”, Power Delivery, IEEE Transactions, 21(1):398-405, 2006.
S. Gupta, et al., “ElectriSense: Single-Point Sensing Using EMI for Electrical Event Detection and Classification in the Home”, Ubicomp 2010, have also proposed the use of the high frequency electromagnetic interference for automatically detecting and classifying the use of electronics devices in the home.
There are also a growing number of research projects that have explored different classification algorithms and feature extraction methods. Neural networks have been used as reported by A. Prudenzi, “A neuron nets based procedure for identifying domestic appliances pattern-of-use from energy recordings at meter panel”, Power Engineering Society Winter Meeting, 2002, IEEE, volume 2, pages 941-946 vol. 2, 2002, and more recently by Hsueh-Hsien Chang, et al., “Load recognition for different loads with the same real power and reactive power in a non-intrusive load-monitoring system”, 12th International Conference on Computer Supported Cooperative Work in Design 2008, pages 1122-1127, IEEE, April 2008.
Genetic algorithms and clustering approaches have also been applied to data acquired from utility meters using an optical sensor as reported by M. Baranski and J. Voss, “Genetic algorithm for pattern detection in NIALM systems”, Systems, Man and Cybernetics, 2004 IEEE International Conference, volume 4, pages 3462-3468 vol. 4, The Hague, The Netherlands, 2004, IEEE. A rule based system was developed to solve the disaggregation problem as reported by Linda Farinaccio and Radu Zmeureanu, “Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses”, Energy and Buildings, 30(3):245-259, August 1999. An attempt to create a general taxonomy for appliance signatures is presented by H. Y. Lam, et al., “A novel method to construct taxonomy electrical appliances based on load signatures”, Consumer Electronics, IEEE Transactions, 53(2) pp. 653-660, 2007, wherein use of clustering techniques and a novel feature set enabled the researchers to identify common traits in the signatures of same-type appliances commonly found in modern residential buildings.
Despite almost two decades of research in the area, techniques for non-intrusively disaggregating the total electrical load of buildings remain in the hands of researchers and have not yet been adopted by society in general. Even though the list of publications in the field is currently large, and still growing, the number of commercial products incorporating some form of NILM, such as “Enetics SPEED” commercially available from Enetics, Inc. of Victor, N.Y., is very limited. One reason for the lack of widespread commercial availability is that previous approaches have relied on custom hardware to monitor the power lines. Typically, these solutions have been very expensive, although recent advances have lowered the cost.
Commercially feasible products which have traditionally relied upon event detection approaches have been further limited by the level of unwanted noise present in the electrical distribution system of modern buildings. Attempts to detect events (e.g., appliance state transitions) in these settings prove to be much harder which makes accuracy more difficult to achieve. Additionally, there is little research showing test results involving modern real world buildings, and even less experimentation addressing the possible energy savings that the approaches could bring in the short, medium, or long term. Thus, commercial motivation for obtaining these products has not been fostered.
Another reason that NILM technology has not progressed from research to development may be a fear that the data derived from NILM would simply be ignored and not be used to modify energy consumption behavior. New research, such as that reported by Corinna Fischer, “Feedback on household electricity consumption: a tool for saving energy?”, Energy Efficiency, 1(1), pp. 79-104, February 2008; however, indicates that real-time, continuous, appliance-level information may be the most effective way to motivate behavior change.
Training the algorithms used in NILM systems has also been an obstacle for wide adoption of the systems. In order for the algorithms to learn how to correctly classify signatures of appliance state transitions, a number of examples must be presented to them. One approach to training algorithms is by having a user manually switch appliances on and off while the monitoring device is in a training mode. This approach is very cumbersome and, in some applications, simply not feasible.
Accordingly, there is a need for improved methods and apparatuses for monitoring energy consumption and for related operations. A system and method that addressed any of the foregoing problems would be beneficial. A system and method that provides data that can be used to raise energy consumption awareness of users would be beneficial. A system and method that non-intrusively monitors a load without the need for an event detector and which directly accounts for the physical dynamics inherently associated with each appliance would be further beneficial.